New software developed at Brown found a mutation that is closely linked to a protein-altering mutation that is virtually absent in populations around the world, but has a frequency of 27 percent in African hunter-gatherer genome data. Courtesy Ramachandran lab

Adaptive Variation

A new machine learning technique helps detect beneficial mutations in population genetic datasets.

Researchers from Brown University have developed a new method for sifting through genomic data in search of genetic variants that have helped populations adapt to their environments.

The technique, dubbed SWIF(r), could be helpful in piecing together the evolutionary history of people around the world, and in shedding light on the evolutionary roots of certain diseases and medical conditions.

SWIF(r) brings several different statistical tests together into a single machine-learning framework. That framework can then be used to scan genomic data from multiple individuals and compute the probabilities that individual mutations or regions of a genome are adaptive.

“These individual statistical techniques are useful, but none of them is particularly powerful on its own,” says Lauren Alpert Sugden ScM’10 PhD’14, a postdoctoral researcher who led the technique’s development. “The method we’ve developed combines those techniques in a way that’s careful and that produces an output that’s easy to interpret.”

“They way we study genetic adaptation now is we start by looking at a particular trait or phenotype, and then we work backward to identify the associated genes and mutations,” Ramachandran says. “This new approach uses data-driven machine learning to start in the genome, searching for adaptive signatures that we can then follow up with more study. So we think this is a way of generating new and interesting hypotheses to test.”

The researchers have made the SWIF(r) code open-source, and they hope that other research groups will use it to explore genomic data from populations worldwide.